Ahead for numbers

The recent purchases of leading analytics companies Omniture and SPSS by tech giants Adobe and IBM show that data analytics is continuing its move towards the mainstream. As a result, researchers can expect to have more and more contact with the field.

As a topic of conversation, analytics divides the world into two groups: those who know a lot about it and those who know very little. It will come as no surprise that the first group is much smaller than the second. But there are always a few exceptions – those people who know enough to separate them from the larger group but not quite enough to gain membership of the elite analytical brains that reside in the smaller group. I belong in this middle ground, and as such I have made it one of my professional aims to try to bring analytics to the research masses.

Where there’s muck there’s brass
The expression ‘Where there’s muck there’s brass’ epitomises analytics. While we don’t mean ‘muck’ in the traditional sense, it does have a parallel in the digital era. As people go about their daily lives in the 21st century they cast off a rich trail of data. When captured and analysed, it can deliver value to businesses – in the pricing of insurance products, the mix of goods that a supermarket stocks on its shelves or the advertising that you get served on a website.

Networks and distributed computing power have enabled businesses to become adept at capturing customer data at every consumer touchpoint. In creating an ongoing dialogue with their customers, businesses now have swathes of data at their disposal. With this come challenges and opportunities. How do you manipulate such quantities of information? And how do you mine that data for commercially useful insights?

In an economic climate where businesses are under severe financial pressure, using existing customer data to identify key relationships and develop marketing hypotheses is a way of making the data that you’ve already obtained work harder – and thus to stretch the research budget. Analytics is the way to turn data into brass, uncovering hidden patterns from which valuable insights can be derived.

Understanding of how analytics can be applied has been clouded by the use of the word as a catch-all term covering anything from the analysis of web-usage data to advanced statistical modelling. Both uses are correct, despite being very different activities, and this hasn’t helped the broader audience grasp some of the fundamentals of analytics.

A simple definition would be that analytics is the process by which a data set is explored to look for underlying relationships (which can then be exploited for future commercial gain). It’s the process of taking raw data and squeezing out the interesting bits – and it is important to think of it as a process rather than as a department, toolkit or piece of software. It demands rigour and attention to detail as well as careful forethought, in order to have a meaningful outcome.

Identifying your objective
The starting point for analytics is to define what we are attempting to do. Is it to identify the purchase drivers of the most valuable customers? Is it to understand the impact of product choice on churn? Either way, we need to decide on a concrete end point, so that we can identify how to start, what hypotheses we have, and what tools and techniques we need to verify or reject those hypotheses.

To be worthwhile, analytics must produce something of meaning and value to a business. Our aim is to link information that we already know about customers with critical business performance indicators. Ideally we will select hard business measures such as sales, but this can bring with it a whole new set of complexities, so to make the process easier we will often select a measure that is a proxy for a hard business success measure – typically primary measures from survey research that relate to customer experience.

One such metric is customer satisfaction, but even this is not without its problems. The relationship between business metrics and satisfaction will never be cut and dried since stated intentions change over time and some customers do not follow through with their claimed behaviour. This can be overcome to some extent by recalibrating a proxy measure to account for this lack of follow-through on the part of some customers, and to account for those external factors that often play a vital role in consumer decision-making.

Having set the appropriate objective, we now have a framework with which to start designing the analytics.

Set out on the right foot
To know where best to start we must create a set of hypotheses that we will either accept or reject as a result of our work. Failure to start any analysis in this way will lead us into trouble. George Soros, the billionaire businessman and philanthropist, is adamant that if you search for evidence that supports what you believe to be true you will find it eventually – but that doesn’t necessarily mean it is true. Creating a framework of hypotheses helps avoid this analytical pitfall.

To get from start to end we need to open up our analytics toolkit and pick an approach that is most appropriate to the objectives of the analytics. At this point we enter a world that is by its nature very technical. We may have more power to analyse data than ever before, but we also have more data to analyse. The technical details of all this are important to practitioners of analytics, but we shouldn’t let them distract us from the fundamentals.